Large Margin Metric Learning

Large Margin Metric Learning is a metric learning framework that belongs to the class of supervised distance metric learning methods, distinguished by its incorporation of a large-margin criterion to maximize the separation between inter-class distances while minimizing intra-class distances in the learned embedding space.

122

Publications

17.1K

Citations

386

Authors

162

Institutions

Publications per year

2017–2026

57

Variants

Lmml

Authors

386

  1. JL
    Jiwen Lu

    Tsinghua University, China

    6 publications · 515 citations

    6

    h-index

  2. JH
    Junlin Hu

    Nanyang Technological University, Singapore

    4 publications · 425 citations

    4

    h-index

  3. KQ
    Kilian Q. Weinberger

    Cornell University, United States

    4 publications · 6.5K citations

    4

    h-index

  4. LW
    Liwei Wang

    Peking University, China

    3 publications · 880 citations

    3

    h-index

  5. YS
    Yoram Singer

    Hebrew University of Jerusalem, Israel

    3 publications · 677 citations

    3

    h-index

1–5 of 386

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 3.3K citations

    11

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 335 citations

    8

    h-index

  3. 5

    h-index

  4. IEEE Transactions on Image Processing

    4 publications · 195 citations

    4

    h-index

  5. IEEE Transactions on Cybernetics

    3 publications · 105 citations

    3

    h-index

1–5